A Guide to Prompt Engineering

A Guide to Prompt Engineering

Jun 22, 2023

Incorporating generative AI, like GPT-4, into products is often challenging. Achieving consistent, quality results is a common hurdle.

The rise in using large language models (LLMs) for product features is evident over the last six months. Our discussions with numerous teams, from startups to established companies, reveal a universal struggle in harnessing LLMs for reliable production use. Teams are navigating the complexities of new technology, evolving tactics, process development, and unforeseen issues stemming from customer interactions and model peculiarities.

Series Objective: Empowering to create better prompts

This blog focuses on prompt crafting, with future pieces to explore testing and evaluation.

Understanding Prompt Engineering

Prompt engineering isn't merely creative phrasing to elicit desired model responses. It encompasses the entirety of practices required for optimal outputs - from crafting appropriate inputs (including dynamic inputs) to ongoing evaluation and iteration.

Effective prompt engineering involves integrating customer experience data, contextualizing with external data, selecting appropriate models, tuning request parameters, and establishing robust testing and evaluation protocols. Techniques like "Chain-of-Thought" and "Self-consistency" also enhance output quality.

Prompt engineering is inherently iterative, demanding continuous refinement.

Key Steps in Prompt Crafting

  1. Planning: Consider the nature of customer input, desired response characteristics, additional data requirements, and the expected output format for seamless integration with your application.

  2. Drafting a Prompt:

    • Instructions: Clear, specific guidelines for the LLM.
    • Input Variables: Placeholders for customer input and other product-related data.
    • Context: Additional information the LLM requires to provide relevant responses.
    • Examples: Demonstrative few-shot examples showcasing the desired response pattern.
    • Output Formatting: Specifying the response format for application compatibility.

Choosing Models and Setting Parameters

Different models offer varying trade-offs in quality, latency, and cost. Model-specific settings can significantly impact responses. Experimentation is key in selecting the most suitable model and tuning request parameters for your needs.

Formatting and Operational Considerations

  • Chat vs. Text Formatting: Adapting the prompt format based on the LLM’s requirements.
  • Response Formatting: Ensuring the output is compatible with your application’s processing capabilities.

Top 6 Insights for Effective Prompt Engineering

  1. Utilize Examples: Incorporate few-shot examples for better results.
  2. Version Control: Regularly update and maintain prompt versions for easy management.
  3. Document Outputs: Keep records of all notable outputs for future reference and testing.
  4. Model Selection: Choose models based on prompt specifics, often using multiple providers.
  5. Monitoring: Regularly check for performance, quality changes, and emerging edge cases.
  6. Feedback Loops: Design mechanisms for customer feedback to continually refine the AI integration.

Example

In conclusion, this guide serves as an introduction to the strategies of prompt engineering. Stay tuned for further insights into prompt engineering.